Secondary Sales Data: The Billion-Dollar Blind Spot in Consumer Goods

A brand manager at a mid-sized biscuit company in Karachi once told me his forecasts were off by 38% almost every quarter. Not because his team was lazy. Because he had no idea what was actually selling at the 64,000 retail shops his distributors claimed to cover.

He knew primary sales. What left the factory. What invoices got raised to distributors. After that? A black hole.

This is the consumer goods data gap nobody in the C-suite wants to talk about at shareholder meetings. And it's quietly burning billions.

The gap between primary and secondary

Primary sales are easy. You ship, you bill, you book revenue. Every ERP on earth handles this beautifully.

Secondary sales — the movement from distributor to retailer — are where visibility collapses. In most emerging markets, distributors operate on paper registers, Excel sheets that get emailed on Fridays (if you're lucky), or sales apps the field team actively avoids because they were designed by someone who's never sat on a motorbike in 42°C heat.

And tertiary sales? What the shopper actually buys off the shelf? Forget it. Outside of modern trade (which is maybe 8-12% of the market in South Asia, Africa, and most of Southeast Asia), nobody's counting.

Here's the thing. A global CPG company can tell you, to the bottle, how much shampoo left its factory in Lahore last Tuesday. But ask them how many bottles sold in Multan that week, and you'll get a confident-sounding number built on three layers of assumptions and a distributor's word.

I used to think this was a tech problem. Build a better app, push it to the field force, done. I was wrong. It's a behavior problem wearing a tech costume.

Why the money bleeds quietly

Let me lay out where the losses actually show up, because "data visibility" sounds abstract until you price it.

Dead inventory at distributor level. When HQ can't see secondary movement, distributors over-order on fast movers (to hit their targets) and under-order on slow movers (to avoid stuck stock). Either way, the brand pays. Either in expired SKUs written off or in stockouts that hand shelf space to a competitor.

Trade spend waste. A 2022 Bain analysis put global trade promotion spend at around 20% of CPG revenue — and estimated 59% of it fails to deliver positive ROI. Why? Because without secondary sales analytics, you can't tell which promotion actually lifted retail offtake versus which one just let distributors load up cheap.

Phantom coverage. Distributors report 5,000 outlets billed. Reality might be 2,800 active, 1,400 inactive, and 800 that exist only in someone's imagination. I've seen audits where 17% of a distributor's "active" retailer list were shops that had closed years ago.

Forecasting that's basically astrology. If your demand plan is built on primary shipments, you're forecasting your own shipping decisions, not consumer demand. That's a feedback loop, not a forecast.

The companies starting to close this gap are doing it with field-level software that captures what's happening at the retailer door, in real time, with geo-tagged visits and SKU-level orders. Platforms like Zivni — which is built specifically for FMCG field teams in markets where distributor data is messy by default — are part of a quiet shift where brands stop asking distributors "what did you sell?" and start knowing the answer themselves.

That shift matters more than it sounds. Because whoever owns the secondary sales data owns the relationship with the retailer. And whoever owns the retailer relationship eventually owns the category.

What actually works (and what doesn't)

A lot of CPG companies have tried to fix this. Most have failed. Here's the pattern I keep seeing.

What doesn't work: mandating distributors use your system. They'll comply for six weeks, then quietly go back to WhatsApp and Excel. Distributors aren't your employees. You can't performance-manage them into behavior change.

What also doesn't work: buying retail audit data from Nielsen or Kantar and calling it a day. Useful for market share directionally. Useless for operational decisions next Tuesday.

What works, based on the brands I've watched actually move the needle:

Put the capture point at your own salesperson, not the distributor. Your field rep walks into the shop anyway. If they're logging the order into a mobile app — with the retailer, the SKUs, the quantity, the location — you've captured secondary sales at the source. The distributor's job becomes fulfillment, not reporting.

Make the app useful for the rep first. If it only serves HQ dashboards, reps will sandbag the data. If it helps them plan their day, remember which retailer owes money, and hit their incentive faster, they'll actually use it.

Start small. One region, one distributor network, one category. Prove the lift in forecast accuracy or trade spend efficiency. Then expand. Every enterprise-wide rollout I've seen attempted in year one has become a cautionary tale by year two.

And honestly, accept that your data will be ugly for the first nine months. Reps gaming the system, GPS pings from chai dhabas, orders logged at 11pm from home. That's fine. Dirty data you can see beats clean data you imagined.

The brands that figure this out in the next five years are going to look at their competitors the way Amazon looks at a department store. Not because they have better products. Because they can see the shelf, and the other guys can't.

Which raises the question worth sitting with — if your company genuinely doesn't know what sold yesterday in half its markets, what exactly are your quarterly plans based on?